import gradio as gr
import numpy as np
import torch
from datasets import load_dataset
from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline


device = "cuda:0" if torch.cuda.is_available() else "cpu"

# load speech translation checkpoint
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2", device=device)
greek_translation_pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-el")

# load text-to-speech checkpoint and speaker embeddings
model_id = "microsoft/speecht5_tts"  # update with your model id
# pipe = pipeline("automatic-speech-recognition", model=model_id)
model = SpeechT5ForTextToSpeech.from_pretrained(model_id)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0)

processor = SpeechT5Processor.from_pretrained(model_id)

model_id_greek = "Sandiago21/speecht5_finetuned_google_fleurs_greek"
model_greek = SpeechT5ForTextToSpeech.from_pretrained(model_id_greek)
processor_greek = SpeechT5Processor.from_pretrained(model_id_greek)

replacements = [
    ("á", "a"),
    ("â", "a"),
    ("ã", "a"),
    ("í", "i"),
    ("á", "a"),
    ("í", "i"),
    ("ñ", "n"),
    ("ó", "o"),
    ("ú", "u"),
    ("ü", "u"),
    ("á", "a"),
    ("ç", "c"),
    ("è", "e"),
    ("ì", "i"),
    ("í", "i"),
    ("ò", "o"),
    ("ó", "o"),
    ("ù", "u"),
    ("ú", "u"),
    ("š", "s"),
    ("ï", "i"),
    ("à", "a"),
    ("â", "a"),
    ("ç", "c"),
    ("è", "e"),
    ("ë", "e"),
    ("î", "i"),
    ("ï", "i"),
    ("ô", "o"),
    ("ù", "u"),
    ("û", "u"),
    ("ü", "u"),
    ("ου", "u"),
    ("αυ", "af"),
    ("ευ", "ef"),
    ("ει", "i"),
    ("οι", "i"),
    ("αι", "e"),
    ("ού", "u"),
    ("εί", "i"),
    ("οί", "i"),
    ("αί", "e"),
    ("Ά", "A"),
    ("Έ", "E"),
    ("Ή", "H"),
    ("Ί", "I"),
    ("Ό", "O"),
    ("Ύ", "Y"),
    ("Ώ", "O"),
    ("ΐ", "i"),
    ("Α", "A"),
    ("Β", "B"),
    ("Γ", "G"),
    ("Δ", "L"),
    ("Ε", "Ε"),
    ("Ζ", "Z"),
    ("Η", "I"),
    ("Θ", "Th"),
    ("Ι", "I"),
    ("Κ", "K"),
    ("Λ", "L"),
    ("Μ", "M"),
    ("Ν", "N"),
    ("Ξ", "Ks"),
    ("Ο", "O"),
    ("Π", "P"),
    ("Ρ", "R"),
    ("Σ", "S"),
    ("Τ", "T"),
    ("Υ", "Y"),
    ("Φ", "F"),
    ("Χ", "X"),
    ("Ω", "O"),
    ("ά", "a"),
    ("έ", "e"),
    ("ή", "i"),
    ("ί", "i"),
    ("α", "a"),
    ("β", "v"),
    ("γ", "g"),
    ("δ", "d"),
    ("ε", "e"),
    ("ζ", "z"),
    ("η", "i"),
    ("θ", "th"),
    ("ι", "i"),
    ("κ", "k"),
    ("λ", "l"),
    ("μ", "m"),
    ("ν", "n"),
    ("ξ", "ks"),
    ("ο", "o"),
    ("π", "p"),
    ("ρ", "r"),
    ("ς", "s"),
    ("σ", "s"),
    ("τ", "t"),
    ("υ", "i"),
    ("φ", "f"),
    ("χ", "h"),
    ("ψ", "ps"),
    ("ω", "o"),
    ("ϊ", "i"),
    ("ϋ", "i"),
    ("ό", "o"),
    ("ύ", "i"),
    ("ώ", "o"),
    ("í", "i"),
    ("õ", "o"),
    ("Ε", "E"),
    ("Ψ", "Ps"),
]

def cleanup_text(text):
    for src, dst in replacements:
        text = text.replace(src, dst)
    return text


def synthesize_speech(text):
    text = cleanup_text(text)
    inputs = processor(text=text, return_tensors="pt")
    speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)

    return gr.Audio.update(value=(16000, speech.cpu().numpy()))


def translate_to_english(audio):
    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate", "language": "english"})
    return outputs["text"]


def synthesise_from_english(text):
    text = cleanup_text(text)
    inputs = processor(text=text, return_tensors="pt")
    speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
    return speech.cpu().numpy()


def translate_from_english_to_greek(text):
    return greek_translation_pipe(text)[0]["translation_text"]


def synthesise_from_greek(text):
    text = cleanup_text(text)
    inputs = processor_greek(text=text, return_tensors="pt")
    speech = model_greek.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
    return speech.cpu()


def speech_to_speech_translation(audio):
    translated_text = translate_to_english(audio)
    translated_text = translate_from_english_to_greek(translated_text)
#     synthesised_speech = synthesise_from_english(translated_text)
#     translated_text = translate_from_english_to_greek(synthesised_speech)
    synthesised_speech = synthesise_from_greek(translated_text)
    synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
    return ((16000, synthesised_speech), translated_text)


title = "Cascaded STST"
description = """
Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Greek. Demo uses OpenAI's [Whisper Large v2](https://huggingface.co/openai/whisper-large-v2) model for speech translation, and [Sandiago21/speecht5_finetuned_google_fleurs_greek](https://huggingface.co/Sandiago21/speecht5_finetuned_google_fleurs_greek) checkpoint for text-to-speech, which is based on Microsoft's
[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech, fine-tuned in Greek Audio dataset:
![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagram of cascaded speech to speech translation")
"""

demo = gr.Blocks()

mic_translate = gr.Interface(
    fn=speech_to_speech_translation,
    inputs=gr.Audio(source="microphone", type="filepath"),
    outputs=[gr.Audio(label="Generated Speech", type="numpy"), gr.outputs.Textbox()],
    title=title,
    description=description,
)

file_translate = gr.Interface(
    fn=speech_to_speech_translation,
    inputs=gr.Audio(source="upload", type="filepath"),
    outputs=[gr.Audio(label="Generated Speech", type="numpy"), gr.outputs.Textbox()],
    examples=[["./example.wav"]],
    title=title,
    description=description,
)

with demo:
    gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])

demo.launch()